Data interaction method and system

Abstract

The invention discloses a data interaction method and system, and relates to the technical field of data processing, and the method comprises the steps: decoupling a data model, interface display and business logic in a scientific research information service process, and building a semantic tag model and a multi-dimensional index structure of scientific research information; extracting a user operation in the client application, packaging the user operation into an intention, transmitting the intention to the semantic tag model, and performing data interaction in combination with the multi-dimensional index structure to obtain a multi-source data set; and based on a responsive view updating mechanism, mapping the multi-source data set to a front-end interface component, and performing one-stop query display of scientific research information. The technical problems that in the prior art, a traditional retrieval method mainly depends on keyword matching, the data integration capacity is insufficient, and the retrieval precision and efficiency are low are solved, and the technical effects that through multi-module linkage and response type updating, the intention of a user is accurately understood, retrieval is conducted in real time, and the information obtaining efficiency and quality are improved are achieved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data interaction method and system. Background Art

[0002] With the explosive growth of scientific research data, researchers are faced with the challenge of efficiently acquiring, processing, and analyzing massive amounts of heterogeneous data. Traditional scientific research information systems often rely on static data models and complex query languages, making them difficult to adapt to ever-changing data needs and user behaviors. Existing systems often tightly couple data models, user interface presentation, and business logic, lacking flexible decoupling mechanisms. Furthermore, when processing complex data queries, they are unable to fully utilize the data's semantic information for intelligent retrieval, resulting in low query efficiency and poor accuracy. Summary of the Invention

[0003] The present application provides a data interaction method and system for solving the technical problems in the prior art that traditional retrieval methods mainly rely on keyword matching, have insufficient data integration capabilities, and have low retrieval accuracy and efficiency.

[0004] The first aspect of the present application provides a data interaction method, which includes: decoupling the data model, interface display and business logic in the scientific research information service process, and establishing a semantic label model and multidimensional index structure of scientific research information; in the client application, extracting user operations through the user interface, and encapsulating the user operations into an intention; passing the intention to the semantic label model, and performing data interaction operations in combination with the multidimensional index structure to obtain multi-source data sets, and encapsulating them into a standardized response data format; based on a responsive view update mechanism, mapping the multi-source data sets to the front-end interface components to perform one-stop query and display of scientific research information.

[0005] The second aspect of the present application provides a data interaction system, which includes: a process decoupling module, which is used to decouple the data model, interface display and business logic in the scientific research information service process, and establish a semantic label model and multidimensional index structure of scientific research information; a user operation extraction module, which is used to extract user operations through the user interface in the client application, and encapsulate the user operations into an intent; a data interaction module, which is used to pass the intent to the semantic label model, and perform data interaction operations in combination with the multidimensional index structure, obtain multi-source data sets, and encapsulate them into a standardized response data format; a query display module, which is used to map the multi-source data sets to the front-end interface components based on the responsive view update mechanism, and perform one-stop query and display of scientific research information.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The present application provides a data interaction method and system, which relate to the field of data processing technology. By decoupling data models, interface displays, and business logic, a semantic tag model and a multi-dimensional index structure are established, user operations are extracted and encapsulated as intentions for data interaction, multi-source data is acquired, and a responsive view update mechanism is utilized to map the data to a front-end interface, thereby realizing a one-stop query and display of scientific research information. This solves the technical problems in the prior art where traditional retrieval methods mainly rely on keyword matching, have insufficient data integration capabilities, and suffer from low retrieval accuracy and efficiency. This achieves the technical effect of accurately understanding user intentions and conducting real-time retrieval through multi-module linkage and responsive updates, thereby improving the efficiency and quality of information acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A flowchart of a data interaction method provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of the data interaction system structure provided in an embodiment of the present application.

[0011] Description of the accompanying drawings: process decoupling module 11, user operation extraction module 12, data interaction module 13, query display module 14. DETAILED DESCRIPTION

[0012] The present application provides a data interaction method and system for solving the technical problems in the prior art that traditional retrieval methods mainly rely on keyword matching, have insufficient data integration capabilities, and have low retrieval accuracy and efficiency.

[0013] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0014] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0015] Example 1, as Figure 1 As shown, the present application provides a data interaction method, which includes:

[0016] P10: Decouple the data model, interface display and business logic in the scientific research information service process, and establish a semantic labeling model and multidimensional index structure for scientific research information.

[0017] Furthermore, to establish a semantic tag model for scientific research information, step P10 of the embodiment of the present application further includes:

[0018] P11: Perform semantic analysis on scientific research information and extract key information elements by analyzing its domain attributes, relationships and semantic context; P12: Define the structure and attributes of semantic tags based on the key information elements and generate a semantic tag set; P13: Associate the semantic tag set with scientific research information data to form a semantic tag model.

[0019] It should be understood that effectively decoupling the data model, interface display, and business logic in the scientific research information service process, and establishing a semantic tag model and multidimensional index structure for scientific research information, ensures that the system has greater flexibility and scalability. In the decoupling process, it is first necessary to build a semantic tag model that can accurately describe scientific research information to facilitate efficient information retrieval and semantic analysis of data.

[0020] Specifically, when building a semantic labeling model, the first step is to extract key information elements from the research data by comprehensively analyzing the domain attributes, relationships, and semantic context of the research information. The domain attributes of research information refer to various features directly related to the research content, such as the name of the research project, research field, and affiliated institution. This information can help determine the basic properties of the data. The relationships between research information describe how different data are connected or dependent on each other, such as collaborative relationships and citation relationships between research projects. Semantic context refers to the academic context of the research information, including an understanding of research directions and technological trends, which helps to accurately analyze and extract relevant elements semantically. Through these analyses, specific key information elements such as project names, participating authors, research objectives, and experimental methods can be extracted from the vast amount of research data.

[0021] Next, after extracting the key information elements, the structure and attributes of the semantic labels need to be defined based on these elements. Each semantic label corresponds to a characteristic of scientific research information. The labels are used to identify each piece of information in the scientific research data, making the data semantically understandable. For example, the label "project name" may have a string-type attribute, while the label "researcher" can have multiple attributes, such as the researcher's name, title, affiliated institution, etc. These attributes together constitute a complete description of the label. In this way, different scientific research information elements are standardized and semantically identified, allowing the system to better understand and process the data. This process is the core part of establishing a semantic labeling model, which ensures that the labeling model has a high level of expression and can accurately describe the various relationships and structures in the scientific research data.

[0022] Finally, after defining the semantic tags and attributes, the generated semantic tag set needs to be associated with specific scientific research information data. This association step is crucial because it will combine each data point in the scientific research information with the corresponding semantic tag, thereby forming a complete semantic tag model. Through this association, each item of scientific research data is clearly labeled and described, so that subsequent data processing, retrieval, analysis and display can be performed efficiently based on these standardized tags. To achieve this goal, the system will use the tag model to match data, ensuring that users can accurately find the data related to their needs based on the tag model when making queries.

[0023] Through the above process, the semantic tagging model not only provides clear structured identification for the data, but also realizes multi-dimensional connections between data, allowing scientific research information to be quickly located and accurately retrieved within massive amounts of data. Through this model, the system can conduct in-depth analysis of scientific research data, helping researchers efficiently obtain valuable information, improve work efficiency, and support scientific research decision-making.

[0024] Furthermore, to establish a multidimensional index structure for scientific research information, step P10 of the embodiment of the present application further includes:

[0025] P14: Determine multiple dimensions of scientific research information, including but not limited to time dimension, space dimension, subject dimension and source dimension; P15: Build an index for each dimension to form a multidimensional index structure, which includes multiple index fields, and each index field is configured with an incremental update mechanism.

[0026] Optionally, in addition to establishing a semantic labeling model for scientific research information, it is also necessary to further construct a multidimensional index structure for scientific research information to achieve efficient data query and accurate retrieval.

[0027] Specifically, first, based on the characteristics of scientific research information, determine multiple dimensions that are applicable to the data set. Scientific research information itself has rich multi-level attributes, so the multi-dimensional characteristics of the data need to be fully considered and defined. Common dimensions include but are not limited to time dimension, space dimension, subject dimension and source dimension. Specifically, the time dimension usually refers to the timestamp of scientific research information, which may include the start time, end time, publication time, etc. of the project; the space dimension involves the geographical location of scientific research activities or data sources, which may include countries, regions, cities, etc.; the subject dimension is used to describe the core areas or research directions of scientific research content, such as "artificial intelligence" and "environmental science"; the source dimension mainly refers to the source of scientific research information, which may be different databases, journals, academic conferences, etc. These dimensions can effectively classify, organize and retrieve scientific research data from multiple perspectives, and are the basis for subsequent data processing and query.

[0028] Next, for each of the above dimensions, the system needs to establish a corresponding index structure. The purpose of the index is to speed up data access and retrieval, which is especially important when facing large-scale data sets. In order to achieve efficient queries, the system will create one or more index fields for each dimension. These fields will contain all key data points under that dimension. For example, the index field of the time dimension may include the starting year, month, quarter, etc. of the project; the index field of the spatial dimension may contain scientific research data entries for various regions. Through these index fields, the system can quickly locate relevant data records in a large amount of scientific research data.

[0029] When building an index, it's important to configure an incremental update mechanism for each index field. This ensures that when data is updated, the system can quickly update existing indexes without re-indexing all the data. For example, when new scientific research data is added to the system, only the relevant index fields need to be updated, without having to re-process all the data, significantly improving the efficiency of data updates. The incremental update mechanism dynamically adjusts the index based on data changes, maintaining high efficiency and real-time performance.

[0030] Ultimately, a multidimensional index structure based on multiple dimensions was established. This structure supports multi-angle and multi-level data queries, allowing users to quickly locate and filter required data based on conditions in different dimensions. Through refined index fields and an incremental update mechanism, the system can efficiently process large amounts of scientific research data, meeting the precise query and real-time update requirements of different users.

[0031] P20: In the client application, user operations are extracted through the user interface and encapsulated into an intent.

[0032] Furthermore, step P20 in this embodiment of the present application further includes:

[0033] P21: During the user interaction process, capture the user's operation behavior data in the user interface; P22: parse the operation behavior data and extract the operation intention information; P23: encapsulate the intention information into an intention object, which includes the operation type, operation parameters and target resource identifier.

[0034] It should be understood that extracting user actions through the user interface and encapsulating them into structured intent objects for subsequent processing and data interaction is crucial for improving system responsiveness and intelligent user interaction, ensuring that user actions are accurately understood and efficiently executed.

[0035] First, during user interaction, the system captures user action data on the interface. This action data includes all user input actions, such as clicking buttons, selecting menu items, and entering text fields. By monitoring user input events, the system can record every user action. For example, if a user clicks a link to a research project or enters a query keyword in the search box, these actions are captured by the system and converted into raw action data. This action data provides the foundation for subsequent intent analysis.

[0036] Next, the captured operational behavior data is parsed to extract intent information. Intent information refers to the actual needs or purposes of the user when performing an operation, such as a user wanting to search for a specific scientific research field or view detailed information about a scientific research project. By analyzing user behavior data, the system can identify the user's operation type (such as query, view, edit, etc.) and the parameters related to the operation (such as search keywords, selected filter conditions, etc.). This parsing process ensures that the system can understand the user's needs and prepare for subsequent data processing.

[0037] Finally, the extracted intent information is encapsulated into an intent object. This intent object contains three key parts: operation type, operation parameters, and target resource identifier. The operation type refers to the specific type of action performed by the user, such as "query," "update," or "delete"; the operation parameters are the specific content or conditions entered by the user, such as query keywords, time range, or scientific research field; the target resource identifier refers to the target object of the user's operation, which may be a scientific research project, document, expert list, etc. In this step, the system packages this information into a structured intent object to facilitate subsequent semantic matching and data interaction operations.

[0038] Through the above steps, the user's interactive behavior can be accurately converted into a structured intent object, so that the user's operation can not only be efficiently captured and understood, but also be correctly responded to and processed in the back-end system.

[0039] P30: The intent is passed to the semantic tag model, and data interaction operations are performed in combination with the multidimensional index structure to obtain multi-source data sets and encapsulate them into a standardized response data format.

[0040] Furthermore, step P30 in the embodiment of the present application further includes:

[0041] P31: Pass the encapsulated intent object to the semantic tag model; P32: Perform semantic matching based on the operation parameters and target resource identifier in the intent object in combination with the semantic tag model to generate a semantic matching result; P33: Through the multi-dimensional index structure, locate and obtain the relevant multi-source data set based on the semantic matching result.

[0042] Optionally, the user's intent object is passed to the semantic tagging model, and data interaction is performed in conjunction with a multi-dimensional index structure to obtain multi-source datasets and encapsulate them into a standardized response data format. Through this process, the system can perform efficient queries based on the user's intent and accurately return the required data.

[0043] First, after a user submits an action intent through a client application, the system passes the encapsulated intent object to the semantic tagging model. This object contains key information such as the user action type, parameters, and target resource identifier. The semantic tagging model matches the user intent with the semantic tags of scientific research information, thereby preliminarily filtering out research information relevant to the user's intent. This process is based on the semantic tag structure and attributes defined in the semantic tagging model, as well as its association with scientific research data.

[0044] Then, when the intent object is passed to the semantic labeling model, the system will perform semantic matching based on the operation parameters and target resource identifiers in the intent object and the semantic labeling model. Specifically, the system will analyze the keywords, subject classifications, time ranges and other parameters in the intent object, and compare them with the semantic labels in the semantic labeling model. For example, if the user's intention is to search for "the application of artificial intelligence in medical imaging diagnosis", the system will match keywords such as "artificial intelligence", "medical imaging" and "diagnosis" with the labels in the semantic labeling model to find scientific research information that is semantically associated with these keywords. At the same time, the system will also consider the target resource identifier to determine the type of resource the user wants to retrieve (such as academic papers, patents, scientific research projects, etc.), and further narrow the matching range. Through this process, the system generates a semantic matching result, that is, it preliminarily screens out a set of scientific research information related to the user's intention.

[0045] After generating the semantic matching results, the system will use the multidimensional index structure to locate and obtain relevant multi-source data sets based on the semantic matching results. The multidimensional index structure provides support for the efficient retrieval of scientific research information. It indexes scientific research information through multiple dimensions (such as time dimension, space dimension, subject dimension and source dimension, etc.). The system will quickly locate relevant data items in the multidimensional index structure based on key information in the semantic matching results, such as subject keywords, time range, etc. For example, if the semantic matching results indicate that the user needs to find scientific research papers on "the application of artificial intelligence in medical imaging diagnosis" in the past five years, the system will find the data range of the past five years through the time dimension index, and then find paper data items related to "artificial intelligence", "medical imaging" and "diagnosis" through the subject dimension index. Finally, the system obtains relevant data sets from multiple data sources (such as different academic databases, patent databases, etc.) and integrates them together.

[0046] After acquiring multi-source datasets, the system encapsulates these data into a standardized response data format. This process ensures data consistency and readability, facilitating subsequent display and processing in client applications. Standardized response data formats may include unified data structures, field definitions, and data encoding methods, enabling data from different sources to be seamlessly integrated and displayed in client applications. This process not only improves the accuracy and efficiency of scientific research information retrieval but also provides users with one-stop query and basic data support for decision-making.

[0047] Furthermore, after obtaining the multi-source data set, step P30 of the embodiment of the present application further includes:

[0048] P34: Perform data cleaning and formatting on the multi-source data set to remove duplicate data and invalid data, and encapsulate the processed data into a standardized response data format.

[0049] In one possible embodiment of the present application, step P30 not only includes the transfer of intent objects, semantic matching, and data acquisition, but also requires further data cleaning and formatting of the acquired multi-source datasets. This process is intended to ensure that the returned data is accurate, complete, and conforms to the system's unified data format requirements, thereby improving data quality and the efficiency of subsequent use.

[0050] Specifically, multi-source datasets are first cleaned to identify and remove duplicate data. Because different data sources may contain identical or similar records, the system ensures the uniqueness of each piece of data through data comparison and deduplication algorithms, using unique identifiers (such as DOIs) or other key information. Simultaneously, the system identifies and processes invalid data, which may contain erroneous, missing, or incomplete information. Through data validation and repair algorithms, the system can remove invalid data or attempt to supplement missing information, thereby improving data accuracy and reliability.

[0051] After data cleansing, the system further formats the data. Because multi-source datasets often have varying data formats and structures, the system needs to convert this data into a unified, standardized response data format. This process includes field alignment, mapping field names and data types from different data sources to standardized field definitions; data encoding unification, ensuring all data uses a uniform encoding format, such as UTF-8; and data structuring, extracting key information from unstructured or semi-structured data and organizing it into structured data records.

[0052] After completing the data cleaning and formatting, the processed data is encapsulated into a standardized response data format, such as JSON or XML format. The standardized response data format facilitates data exchange and communication between the system and other modules or platforms, ensuring data compatibility and availability. This format not only ensures the consistency and scalability of the data, but also facilitates client applications to parse and display, thereby providing users with high-quality and consistent scientific research information retrieval results. Through this coherent and executable processing flow, this application can effectively improve the performance and user experience of the scientific research information retrieval system, and provide researchers with one-stop high-quality scientific research information services.

[0053] P40: Based on a responsive view update mechanism, the multi-source dataset is mapped to the front-end interface component to provide a one-stop query and display of scientific research information.

[0054] Furthermore, step P40 in this embodiment of the present application further includes:

[0055] P41: Parse the data content according to the standardized response data format; P42: Map the parsed data content to the front-end interface component, and dynamically update the display content of the front-end interface component through the responsive view update mechanism.

[0056] Specifically, based on a responsive view update mechanism, the acquired multi-source datasets are mapped to front-end interface components to achieve a one-stop query and display of scientific research information. This step ensures that the system updates the front-end interface in real time according to user query requirements and provides an efficient data display and interactive experience.

[0057] Specifically, the system first parses the acquired multi-source datasets according to a standardized response data format, extracting key information from the data content. This standardized response data format provides a clear structure and field definitions for data parsing, enabling the system to quickly and accurately identify and extract key components of the data, such as the title, author, publication date, and abstract of a research paper, or the name, person in charge, research cycle, and achievements of a research project. This parsing process is the foundation for data visualization, ensuring that the data is correctly mapped to the front-end interface components.

[0058] Subsequently, the system maps the parsed data content to the front-end interface components. The front-end interface components are visual interfaces for users to interact with the system, including various layouts, tables, lists, charts, and other forms for displaying scientific research information. Through the responsive view update mechanism, the system can dynamically update the display content of the front-end interface components based on user operations and data changes. For example, when a user enters a new keyword in the search box and submits a query request, the system will re-acquire and parse the data based on the new query intent, and then map the updated data content to the front-end interface components in real time. Users can see the latest search results without reloading the page. This dynamic update mechanism not only improves the response speed of the system, but also enhances the interactive experience between users and the system, allowing users to browse and analyze scientific research information more conveniently.

[0059] Furthermore, the responsive view update mechanism automatically adjusts the layout and style of front-end interface components based on different devices and screen sizes, ensuring a good visual experience and user experience across a wide range of devices (e.g., desktop computers, tablets, and mobile phones). For example, larger screens can display more detailed information and complex charts, while smaller screens can automatically adjust the layout to highlight key information and optimize the display.

[0060] A responsive view update mechanism dynamically adjusts interface content based on real-time user query results and operations, providing a seamless data display experience. This mechanism ensures efficient linkage between data and the interface, enhancing the user interaction experience while ensuring real-time and accurate interface performance. For example, when a user selects a new query condition, the system updates the query results according to the new standardized response data format, and the front-end interface promptly displays the new data, ensuring that users see the most relevant and up-to-date scientific research information.

[0061] Through the above steps, the entire process from data acquisition to dynamic interface update is realized, ensuring that scientific research information can be presented to users quickly and accurately on the front-end interface and can be updated in real time with user interaction.

[0062] In summary, the embodiments of the present application have at least the following technical effects:

[0063] This application decouples the data model, interface display, and business logic in the scientific research information service process, establishes a semantic labeling model and a multidimensional index structure, and improves the scalability of the system and the accuracy of data retrieval. The client extracts operations through the user interface and encapsulates them as intents, passes them to the semantic labeling model, and combines them with the multidimensional index to interact with data, obtain and standardize multi-source data sets. Finally, using a responsive view update mechanism, the data is mapped to the front-end interface components to achieve a one-stop query and display of scientific research information, improving the user interaction experience and the real-time performance of data display.

[0064] The technical effect of accurately understanding user intentions and conducting real-time retrieval through multi-module linkage and responsive updates has been achieved, thereby improving the efficiency and quality of information acquisition.

[0065] Example 2, based on the same inventive concept as a data interaction method in the above embodiment, such as Figure 2 As shown, the present application provides a data interaction system. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0066] The process decoupling module 11 is used to decouple the data model, interface display and business logic in the scientific research information service process, and establish a semantic label model and multi-dimensional index structure of scientific research information.

[0067] The user operation extraction module 12 is used to extract user operations through the user interface in the client application and encapsulate the user operations into an intent.

[0068] The data interaction module 13 is used to transfer the intent to the semantic tag model, perform data interaction operations in combination with the multi-dimensional index structure, obtain multi-source data sets, and encapsulate them into a standardized response data format.

[0069] The query display module 14 is used to map the multi-source data set to the front-end interface component based on a responsive view update mechanism to perform a one-stop query display of scientific research information.

[0070] Furthermore, the process decoupling module 11 is further configured to perform the following steps:

[0071] By analyzing the domain attributes, relationships and semantic context of scientific research information, semantic analysis is performed on the scientific research information to extract key information elements; based on the key information elements, the structure and attributes of semantic tags are defined to generate a semantic tag set; the semantic tag set is associated with the scientific research information data to form a semantic tag model.

[0072] Furthermore, the process decoupling module 11 is further configured to perform the following steps:

[0073] Determine multiple dimensions of scientific research information, including but not limited to time dimension, space dimension, subject dimension and source dimension; construct an index for each dimension to form a multidimensional index structure, wherein the multidimensional index structure includes multiple index fields, and each index field is configured with an incremental update mechanism.

[0074] Furthermore, the user operation extraction module 12 is further configured to perform the following steps:

[0075] During the user interaction process, the user's operation behavior data in the user interface is captured; the operation behavior data is parsed to extract the operation intention information; the intention information is encapsulated into an intention object, which includes the operation type, operation parameters and target resource identifier.

[0076] Furthermore, the data interaction module 13 is further configured to perform the following steps:

[0077] The encapsulated intent object is passed to the semantic tag model; semantic matching is performed based on the operation parameters and target resource identifiers in the intent object in combination with the semantic tag model to generate a semantic matching result; through a multi-dimensional index structure, the relevant multi-source data set is located and obtained based on the semantic matching result.

[0078] Furthermore, the data interaction module 13 is further configured to perform the following steps:

[0079] The multi-source data set is cleaned and formatted to remove duplicate data and invalid data, and the processed data is packaged into a standardized response data format.

[0080] Furthermore, the query display module 14 is further configured to perform the following steps:

[0081] According to the standardized response data format, the data content is parsed; the parsed data content is mapped to the front-end interface component, and the display content of the front-end interface component is dynamically updated through a responsive view update mechanism.

[0082] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0084] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A data interaction method, characterized in that: The method comprises: Decouple the data model, interface display, and business logic in the scientific research information service process, and establish a semantic labeling model and multi-dimensional index structure for scientific research information; In the client application, user operations are extracted through the user interface and encapsulated into an intent; The intent is passed to the semantic tag model, and data interaction operations are performed in combination with the multidimensional index structure to obtain multi-source data sets and encapsulate them into a standardized response data format; Based on a responsive view update mechanism, the multi-source data sets are mapped to front-end interface components to provide a one-stop query and display of scientific research information.

2. A data interaction method according to claim 1, characterized in that: Decouple the data model, interface display, and business logic in the scientific research information service process and establish a semantic labeling model for scientific research information, including: By analyzing the domain attributes, relationships and semantic context of scientific research information, semantic analysis is performed on scientific research information to extract key information elements; Defining the structure and attributes of semantic tags based on the key information elements, and generating a semantic tag set; The semantic tag set is associated with scientific research information data to form a semantic tag model.

3. A data interaction method according to claim 2, characterized in that: Establish a multidimensional index structure for scientific research information, including: Identify multiple dimensions of scientific research information, including but not limited to time, space, subject, and source dimensions; An index is constructed for each dimension to form a multidimensional index structure, which includes multiple index fields, and each index field is configured with an incremental update mechanism.

4. A data interaction method according to claim 1, characterized in that: Extract user actions from the user interface and encapsulate them into an intent, including: During user interaction, capture user operation behavior data in the user interface; Analyzing the operation behavior data to extract operation intention information; The intent information is encapsulated into an intent object, which includes an operation type, operation parameters and a target resource identifier.

5. A data interaction method according to claim 4, characterized in that: The intent is transferred to the semantic tag model, and data interaction operations are performed in combination with the multidimensional index structure to obtain a multi-source data set, including: Pass the encapsulated intent object to the semantic labeling model; According to the operation parameters and target resource identifier in the intention object, semantic matching is performed in combination with the semantic tag model to generate a semantic matching result; Through the multi-dimensional index structure, the relevant multi-source data sets are located and acquired according to the semantic matching results.

6. A data interaction method according to claim 5, characterized in that: After obtaining the multi-source data set, the method further includes: The multi-source data set is cleaned and formatted to remove duplicate data and invalid data, and the processed data is packaged into a standardized response data format.

7. A data interaction method according to claim 1, characterized in that: Based on a responsive view update mechanism, the multi-source datasets are mapped to front-end interface components to provide a one-stop query and display of scientific research information, including: Parsing data content according to the standardized response data format; Map the parsed data content to the front-end interface components, and dynamically update the display content of the front-end interface components through the responsive view update mechanism.

8. A data interaction system, characterized in that: The system comprises: A process decoupling module is used to decouple the data model, interface display and business logic in the scientific research information service process, and establish a semantic label model and multidimensional index structure for scientific research information; A user operation extraction module, which is used to extract user operations through the user interface in the client application and encapsulate the user operations into an intent; A data interaction module, configured to transmit the intent to the semantic tag model, perform data interaction operations in combination with the multidimensional index structure, obtain multi-source data sets, and encapsulate them into a standardized response data format; A query display module is used to map the multi-source data set to a front-end interface component based on a responsive view update mechanism to perform a one-stop query and display of scientific research information.

Images